BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VEVENT CLASS:PUBLIC CREATED:20181029T125804Z DESCRIPTION:Define the future of automated driving and discover the next er a of mobility | March 11 – 12\, 2019 | TITANIC Chaussee Berlin\, German y \n \nTech.AD Berlin Key Topics\ nThe Tech.AD Technical Sessions\n· What is a highly sc alable architecture and function path from Assisted to Automated Driving?\ n· New SPAD sensor technology ‒ from Architectures t o Applications\n· Relaying on LIDAR Sensor Technology to Master Complex Traffic Scenarios ‒ Assessing future opportunities and challenges with LiDAR adoption for vehicle perception\n· What is the potential role of the tire technology for safe autonomous driving? How can the tire sensor enhance automated driving?\n· On-The-Road Beta Testing and verification for autonomous driving – What are the challenges? In addition\, how can recent advances in mode ling\, verification\, and implementation technologies simultaneously reduc e and accelerate the required testing and verification effort?\n· What are the requirements future complexity driven by Level 3 & 4 automation?\n· Architecture needs for automated dr iving ‒ How do you envisage the architecture that will need to be implem ented to deliver automated driving (Level 3 and above)?\n· Vehicle Automation and the importance of Radar Sensors – Why is env ironmental perception and understanding of the backbone of driverless cars important?\n· Novel LiDAR sensing technology: A new a pproach to detection and ranging- Implementation of high-performance LiDAR flash and LiDAR scanning micro-mirrors for autonomous driving application s\n· From Fail-Safe components to operational safe sys tems for Automated Driving ‒ How is operational safety looking beyond IS O 26262?\n· How is System-Theoretic Safety & Security Analysis with STPA-Sec working & which are the safety architecture solutio ns needed to be discussed?\n· Cognitive Cars & Vehicle s – Challenges for imaging\, perception sytems and AI\n· Expectations on AI – Detection and recognition of multiple objects\ , improved perception\, reduced power consumption\, improved object classi fication\, recognition and prediction of actions\, and reduction of devel opment time of ADAS systems.\n· Deep learning architec ture in embedded systems\n \nThe Tech.AD Business & Technology Sessions\n · Level 3 & 4 – Challenges for virtual & real life t esting\, validation & simulation\n· Will software be t he future differentiator of automated cars? In addition\, what does it nee ds? Scalable\, platform-independent software architectures for ADAS and au tomated driving ‒ Is the V Model still valid or do we need to restructur e the development process?\n· Fail-operational automat ed driving architectures – What are the challenges in development and te sting? In addition in which extent will the number of test cases increase with a higher level automation?\n· How does automated driving impact the user experience? Autonomous driving from an end-user an d usability perspective\n· Automated parking ‒ Archi tecture/integration\, modular functional development & strategic implicati ons\n· Deep Driving ‒ Artificial intelligence\, Deep Learning & Deep Driving in Automotive ‒ What are state of the art algor ithms for computer vision and machine learning including those most import ant for the automotive industry?\n· What are the newes t concepts\, challenges\, use cases & game changers to the new AUTOSAR Ada ptive Platform for Connected and Autonomous Vehicles?\n· Safety and Risk Issues for Self Driving Cars ‒ Consider the requireme nts of cyber security in a variety of system layouts e.g. integrated netwo rk and centralised processor\, to establish what layout offers the most se cure architecture\n· Ergonomic Design of the Vehicle M otion in an Automated Driving Car – What is a concept for the design of an automated driving system that uses the driver’s motion perception to feed back the automation system’s state and intention & which design & i nteraction concept do we need in which level of automation according to SA E?\n· Embracing the ecosystem of Automated Mobility on demand ‒ Explore how Automated Mobility on Demand (AMoD) will fill soci etal gaps for first mile and last mile public transportation\, redefining the field of mobility\n· Artificial Intelligence for A utonomous Systems: Hype vs. Reality\n \n \nSpeaker Panel: https://autonomo us-driving-berlin.com/speaker/\nWhat can you expect @ Tech.AD Berlin ? htt ps://autonomous-driving-berlin.com/who-why/\nHow to attend? Tickets: https ://autonomous-driving-berlin.com/book-now/\n DTEND;VALUE=DATE:20190313 DTSTAMP:20180727T100213Z DTSTART;VALUE=DATE:20190311 LAST-MODIFIED:20181029T125804Z LOCATION:TITANIC Chaussee Berlin\, Germany PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=de:Automotive Tech.AD Berlin TRANSP:TRANSPARENT UID:040000008200E00074C5B7101A82E0080000000070F50A85A025D401000000000000000 010000000741E751F4E188C46BDECE5A15B40B079 X-ALT-DESC;FMTTYPE=text/html:

< span class=SpellE>Define the future of autom ated driving and discover the next era o f mobility   | March 11 – 12\, 2019 | TITANIC Chaussee Berlin\, Germany < \;https://autonomous-driv ing-berlin.com/>\;

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Tech.AD Berlin Key Topics

The Tech.AD Tech nical Sessions

< span style='font-family:Symbol\;mso-fareast-font-family:Symbol\;mso-bidi-f ont-family:Symbol'>· \; \; \; \; \; \; \;  \; \; \; \; \; \; \; \; \; \; \;&n bsp\; What is a highly scalable architecture and function path from Assisted to Automated Driving?

· \; \; \; \; \; \;&nb sp\; \; \; \; \; \; \; \; \; \; \;  \; \; New SPAD sensor technology ‒\; from Architectures to Applications

· \; \;&nbs p\; \; \; \; \; \; \; \; \; \; \;& nbsp\; \; \; \; \; \; < span class=SpellE>Relaying on LIDAR Sensor Technology to Master Complex Traffic Scenarios ‒\; Assessing future opportunities and challenges with LiDAR adoption for vehicle per ception

< span style='font-family:Symbol\;mso-fareast-font-family:Symbol\;mso-bidi-f ont-family:Symbol'>· \; \; \; \; \; \; \;  \; \; \; \; \; \; \; \; \; \; \;&n bsp\; What is the potential role of the tire technology < span class=SpellE>for safe autonomous driving? How can the tire sensor enhance automated driving?

· \; \; \; \; \; \; \ ; \; \; \; \; \; \; \; \; \; \;&nb sp\; \; On-The-Road Beta Testing and verif ication for autonomous drivingWhat are the challenges? In addition\, how can re cent advances in model ing\, verification\, a nd implementation tech nologies simultaneously reduce and accelerat e the required testing and verification effort?< /p>

· \ ; \; \; \; \; \; \; \; \; \; \;&nb sp\; \; \; \; \; \; \; \; What are the requirements future complexity driven by Level 3 &\; 4 automation?

· \; \; \; \; \;&n bsp\; \; \; \; \; \; \; \; \; \; \ ; \; \; \; A rchitecture needs for< /span> automated driving ‒\; How do you envisage the architecture that will need to be implemented to deliver automated driving (Level 3 and above)?

· \; \; \; \; \;&nbs p\; \; \; \; \; \; \; \; \; \; \;& nbsp\; \; \; Veh icle Automation and th e importance of Radar Sensors – Why is environmental perception and understanding of the backbone of driverless cars important?

· \;  \; \; \; \; \; \; \; \; \; \; \;&n bsp\; \; \; \; \; \; \; Novel LiDAR sensing technology: A new approach to detection and< /span> ranging- Implementation of high-performan ce LiDAR flash and LiDAR scanning micro-mirrors for autonomous drivin g applications

· \; \; \;  \; \; \; \; \; \; \; \; \; \;&nbs p\; \; \; \; \; \; From Fail-Safe components to operational safe systems for Automated Driving ‒\; How is operational safety looking beyond ISO 26262?

· \; \; \; \; \; \;  \; \; \; \; \; \; \; \; \; \;&nbs p\; \; \; How is System-Theoretic Safety &\; Security Analysis with STPA-Sec working &\; which are the safety architectu re solutions needed to be discussed?

· \; \; \; \; \; \;& nbsp\; \; \; \; \; \; \; \; \; \;  \; \; \; Cogniti ve Cars &\; VehiclesChallenges for i maging\, perception sy tems and AI

· \; \; \;&nb sp\; \; \; \; \; \; \; \; \; \; \;  \; \; \; \; \; Expectations on AI – Detection and recognition of multiple objects\, improved perception\, reduced power consumption\, improved object classification\, recognition and prediction of actions\, and reduction of  devel opment time of ADAS sy stems.

· \; \; \; \; \; \; \; \ ; \; \; \; \; \; \; \; \; \; \;&nb sp\; Deep learning architecture in embedded systems

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The Tech.AD Business &\; Technology Sessions

· \; \; \;&nb sp\; \; \; \; \; \; \; \; \; \; \;  \; \; \; \; \; Level 3 &\; 4 – Challenges for< /span> virtual &\; real li fe testing\, validatio n &\; simulation

· \; \;&n bsp\; \; \; \; \; \; \; \; \; \; \ ; \; \; \; \; \; \; Will software be the future differentiator of automated cars? In add ition\, what does it needs? Scalable\, platform-independent software architectures for ADAS and automated driving ‒\; Is the V Model still val id or do we need to restr ucture the development process?

· \; \; \;  \; \; \; \; \; \; \; \; \; \; \;&n bsp\; \; \; \; \; Fail-oper ational automated driving architecturesWhat are the challenges in development and testing? In addition in which extent will the nu mber of test cases increase with a higher level automation?

· \; \;&n bsp\; \; \; \; \; \; \; \; \; \; \ ; \; \; \; \; \; \; How does automated driving impact the user experience? Autonomous driving from an e nd-user and usability perspective

·  \; \; \; \;&nb sp\; \; \; \; \; \; \; \; \; \; \;  \; \; \; \; Automated parking ‒\; Architecture/integration\, modu lar functional development &\; strategic implica tions

· \; \; \; \; \; \; \; \;  \; \; \; \; \; \; \; \; \; \;&nbs p\; Deep Driving ‒\; Artificial intelligence\, Deep Lear ning &\; Deep Driving in Automotive ‒\; What are state of the art algorithms for co mputer vision and machine learning including those most important for the automotive industry?

· \; \; \;&nbs p\; \; \; \; \; \; \; \; \; \; \;& nbsp\; \; \; \; \; What are th e newest concepts\, challenges\, use cases &\; game changers to the new AUTOSAR Adaptive Platform for C onnected and Autonomou s Vehicles?

· \; \; \;&nb sp\; \; \; \; \; \; \; \; \; \; \;  \; \; \; \; \; Safety and Risk Issues for Self Driving Cars &# 8210\; Consider the requirements of cyber security in a variety of sy stem layouts e.g. inte grated network and centralised processor\, to establish what layout offers the most< /span> secure architecture

· \; \; \; \; \; \; \; \; \;& nbsp\; \; \; \; \; \; \; \; \; \; Ergonomic Design of the Vehicle Motion in an Automated Driving Car – What is a concept f or the design of an automated driving system that uses the dri ver’s motion percept ion to feed bac k the automation system’s state and intention &\; which design &\; interaction < span class=SpellE>concept do we need in which level of automation< /span> according to SA E?

· \; \; \; \; \; \; \; \; \;&nb sp\; \; \; \; \; \; \; \; \; \; Embracing the ecosystem of Automated Mobility on demand ‒\; Explore how Automated Mobility on Demand (AMoD) will fill societal gaps for first mile and last mile public transportation\, redefining the field of mobili ty

· \; \; \; \; \; \; \; \;&nb sp\; \; \; \; \; \; \; \; \; \; \; Artificial Intelligence for Autonomous Systems: Hype vs. Reality

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Speaker Panel: https://autonomous-driving-berlin.com/speaker/< /a>

What can you expect @ Tech.AD Berlin ? < a href="https://autonomous-driving-berlin.com/who-why/">https://autonomous -driving-berlin.com/who-why/

How to attend? Tickets: https://autonomous-driving-be rlin.com/book-now/

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